Download Data Cleansing Master Class in Python torrent

Data Cleansing Master Class in Python

Torrent:	Data Cleansing Master Class in Python
Description:	Description Welcome to Data Cleansing Master Class in Python. Data preparation may be the most important part of a machine learning project. It is the most time consuming part, although it seems to be the least discussed topic. Data preparation, sometimes referred to as data preprocessing, is the act of transforming raw data into a form that is appropriate for modeling. Machine learning algorithms require input data to be numbers, and most algorithm implementations maintain this expectation. Therefore, if your data contains data types and values that are not numbers, such as labels, you will need to change the data into numbers. Further, specific machine learning algorithms have expectations regarding the data types, scale, probability distribution, and relationships between input variables, and you may need to change the data to meet these expectations. In the course you’ll learn: The importance of data preparation for predictive modeling machine learning projects. How to prepare data in a way that avoids data leakage, and in turn, incorrect model evaluation. How to identify and handle problems with messy data, such as outliers and missing values. How to identify and remove irrelevant and redundant input variables with feature selection methods. How to know which feature selection method to choose based on the data types of the variables. How to scale the range of input variables using normalization and standardization techniques. How to encode categorical variables as numbers and numeric variables as categories. How to transform the probability distribution of input variables. How to transform a dataset with different variable types and how to transform target variables. How to project variables into a lower-dimensional space that captures the salient data relationships. This course is a hands on-guide. It is a playbook and a workbook intended for you to learn by doing and then apply your new understanding to the feature engineering in Python. To get the most out of the course, I would recommend working through all the examples in each tutorial. If you watch this course like a movie you’ll get little out of it. In the applied space machine learning is programming and programming is a hands on-sport. Thank you for your interest in Data Cleansing Master Class in Python. Let’s get started! Who this course is for: You are serious about become a machine learning engineer in the real-world. Requirements You’ll need a really solid foundation in Python. You’ll need to understand the basics of machine learning. Last Updated 7/2021
Downloads:	187
Category:	Other /Tutorials
Size:	1.4 GB
Show Files »	Data Cleansing Master Class in Python [TutsNode.com] - Data Cleansing Master Class in Python 02 Foundations 007 Machine Learning is Mostly Data Preparation.mp4 (40.9 MB) 006 Raw Data.en.srt (8.2 KB) 001 Introducing Data Preparation.en.srt (2.8 KB) 004 Choosing a Data Preparation Technique.en.srt (2.7 KB) 011 Common Data Preparation Tasks - Feature Engineering.en.srt (2.2 KB) 012 Common Data Preparation Tasks - Dimensionality Reduction.en.srt (2.9 KB) 002 The Machine Learning Process.en.srt (5.4 KB) 013 Data Leakage.en.srt (1.1 KB) 014 Problem With Naïve Data Preparation.en.srt (5.2 KB) 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.en.srt (2.4 KB) 018 Data Cleansing Master Class - Data Preparation With Training and Testing Sets.zip (1.5 KB) 005 What is Data in Machine Learning_.en.srt (4.7 KB) 001 Introducing Data Preparation.mp4 (36.4 MB) 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.en.srt (4.2 KB) 007 Machine Learning is Mostly Data Preparation.en.srt (4.1 KB) 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.en.srt (3.9 KB) 010 Common Data Preparation Tasks - Data Transforms.en.srt (3.9 KB) 003 Data Preparation Defined.en.srt (3.8 KB) 008 Common Data Preparation Tasks - Data Cleansing.en.srt (3.7 KB) 009 Common Data Preparation Tasks - Feature Selection.en.srt (3.5 KB) 018 Case Study_ Data Leakage_ K-Fold Correct Approach.en.srt (3.1 KB) 003 Data Preparation Defined.mp4 (30.2 MB) 004 Choosing a Data Preparation Technique.mp4 (25.9 MB) 014 Problem With Naïve Data Preparation.mp4 (24.9 MB) 008 Common Data Preparation Tasks - Data Cleansing.mp4 (21.7 MB) 011 Common Data Preparation Tasks - Feature Engineering.mp4 (21.6 MB) 006 Raw Data.mp4 (20.5 MB) 005 What is Data in Machine Learning_.mp4 (17.9 MB) 015 Case Study_ Data Leakage_ Train_Test_Split Naïve Approach.mp4 (16.5 MB) 017 Case Study_ Data Leakage_ K-Fold Naïve Approach.mp4 (14.3 MB) 002 The Machine Learning Process.mp4 (14.3 MB) 018 Case Study_ Data Leakage_ K-Fold Correct Approach.mp4 (12.8 MB) 016 Case Study_ Data Leakage_ Train_Test_Split Correct Approach.mp4 (9.5 MB) 013 Data Leakage.mp4 (8.8 MB) 009 Common Data Preparation Tasks - Feature Selection.mp4 (7.9 MB) 010 Common Data Preparation Tasks - Data Transforms.mp4 (4.7 MB) 012 Common Data Preparation Tasks - Dimensionality Reduction.mp4 (4.1 MB) 01 Introduction 001 Course Introduction.en.srt (2.8 KB) 003 Is this Course Right for You_.en.srt (1.7 KB) 002 Course Structure.en.srt (3.6 KB) 002 Course Structure.mp4 (23.9 MB) 001 Course Introduction.mp4 (22.6 MB) 003 Is this Course Right for You_.mp4 (1.6 MB) 05 Data Transforms 085 Power Transforms.zip (50.4 KB) 072 Data Rescaling .zip (25.0 KB) 089 Polynomial Feature Transform.zip (14.2 KB) 005 Robust Scaling Data.en.srt (5.6 KB) 019 Polynomial Features.en.srt (5.2 KB) 020 Polynomial Transform on Sonar Dataset.en.srt (5.2 KB) 013 OrdinalEncoder Transform on Breast Cancer Dataset.en.srt (5.0 KB) 008 Nominal and Ordinal Variables.en.srt (4.4 KB) 015 Power Transform on Contrived Dataset.en.srt (3.6 KB) 009 Ordinal Encoding.en.srt (3.3 KB) 012 Dummy Variable Encoding.en.srt (3.1 KB) 017 Box-Cox on Sonar Dataset.en.srt (3.1 KB) 014 Make Distributions More Gaussian.en.srt (2.9 KB) 011 One-Hot Encoding.en.srt (2.9 KB) 016 Power Transform on Sonar Dataset.en.srt (2.9 KB) 001 Scale Numerical Data.en.srt (2.7 KB) 021 Effect of Polynomial Degrees.en.srt (2.7 KB) 018 Yeo-Johnson on Sonar Dataset.en.srt (2.7 KB) 004 StandardScaler Transform.en.srt (2.6 KB) 002 Diabetes Dataset for Scaling.en.srt (2.5 KB) 003 MinMaxScaler Transform.en.srt (2.3 KB) 006 Robust Scaler Applied to Dataset.en.srt (2.2 KB) 007 Explore Robust Scaler Range.en.srt (1.6 KB) 010 One-Hot Encoding Defined.en.srt (1.3 KB) 008 Nominal and Ordinal Variables.mp4 (26.0 MB) 019 Polynomial Features.mp4 (20.7 MB) 020 Polynomial Transform on Sonar Dataset.mp4 (20.6 MB) 013 OrdinalEncoder Transform on Breast Cancer Dataset.mp4 (17.1 MB) 005 Robust Scaling Data.mp4 (16.5 MB) 017 Box-Cox on Sonar Dataset.mp4 (11.7 MB) 016 Power Transform on Sonar Dataset.mp4 (10.9 MB) 004 StandardScaler Transform.mp4 (10.5 MB) 018 Yeo-Johnson on Sonar Dataset.mp4 (9.6 MB) 003 MinMaxScaler Transform.mp4 (8.9 MB) 002 Diabetes Dataset for Scaling.mp4 (8.7 MB) 015 Power Transform on Contrived Dataset.mp4 (8.5 MB) 006 Robust Scaler Applied to Dataset.mp4 (8.4 MB) 021 Effect of Polynomial Degrees.mp4 (7.5 MB) 009 Ordinal Encoding.mp4 (7.0 MB) 012 Dummy Variable Encoding.mp4 (7.0 MB) 011 One-Hot Encoding.mp4 (6.8 MB) 007 Explore Robust Scaler Range.mp4 (5.6 MB) 001 Scale Numerical Data.mp4 (5.1 MB) 014 Make Distributions More Gaussian.mp4 (4.0 MB) 010 One-Hot Encoding Defined.mp4 (1.7 MB) 03 Data Cleansing 030 housing.csv (47.9 KB) 023 Sparse Column Identification and Removal.zip (10.1 KB) 041 IterativeImputer and Different Number of Iterations.zip (8.3 KB) 038 KNNImputer and Model Evaluation Different K-Values.zip (8.0 KB) 036 Comparing Different Imputed Statistics.zip (7.4 KB) 010 Mark Missing Values.en.srt (6.8 KB) 007 Remove Outliers - The Standard Deviation Approach.en.srt (5.4 KB) 009 Automatic Outlier Detection.en.srt (5.2 KB) 016 K-Nearest Neighbors Imputation.en.srt (5.1 KB) 013 Mean Value Imputation.en.srt (4.9 KB) 001 Data Cleansing Overview.en.srt (2.2 KB) 003 Identify Columns with Few Values.en.srt (4.2 KB) 006 Defining Outliers.en.srt (2.7 KB) 034 Statistical Imputation With SimpleImputer.zip (1.7 KB) files
Added:	2021-08-01 11:08:38
Language:	English
Peers:	Seeders : 23 , Leechers : 5
Release name:	Data Cleansing Master Class in Python
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